Walden University DDHA 8800 Simulation of Telemedicine Technologies
Walden University DDHA 8800 Simulation of Telemedicine Technologies
Part 1
Simulation of Telemedicine
It may come as no surprise that advances in technology have had a dramatic impact on healthcare delivery. Advances in health information technology, such as patient portals; electronic health records (EHRs) or electronic medical records (EMRs); and real-time coordination of patient care, etc., all have greatly contributed to enhancements in healthcare delivery. However, they too presented several challenges to healthcare administration leaders and clinical staff in how to best orient and implement such technology to enhance healthcare delivery. Walden University DDHA 8800 Simulation of Telemedicine Technologies
One such advancement in healthcare technology concerns the use of telemedicine to provide patient care and treatment. While delivery of patient care is usually a direct transaction, interfacing with patients and physicians virtually, or at a distance, could greatly enhance how healthcare services are delivered for certain situations, such as disaster events or in rural locales. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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For this Discussion, review the resources for this week. Reflect on the Torabi et al. (2016) article in the resources for this week and consider the distributions the authors selected for the given simulation.
Post a description of the distributions selected by the authors in the Torabi et al. (2016) article, and then explain whether the distributions selected are appropriate for practice, and why. Explain what was done well in the study, as well as areas of weakness for the considerations described by the authors. Be specific and provide examples. Walden University DDHA 8800 Simulation of Telemedicine Technologies
Part 2
More Advanced Simulation in Health Care
Simulation in health care often involves more than trivial skills. For example, modeling all outpatient clinic operations in a particular facility would probably involve the use of multiple probability distributions and many calculations. Verifying and validating the simulation requires even more technical prowess. Healthcare administration leaders and decision makers must have the knowledge, skill, and abilities to build and understand these simulations.
For this Assignment, review the resources for this week, and reflect on the advanced simulation techniques highlighted. Consider how these advanced simulation techniques might apply to specific simulation models in a health services organization, and then complete the problems assigned for the Assignment.
The Assignment: (4–5 pages)
- Complete Problem 45 (Prizdol prescription drug) on page 887 of your course text.
Note: You will be using Excel and @Risk for this Assignment.
Submit your answers and embedded analysis as a Microsoft Word management report.
ORIGINAL CONTRIBUTION
Monte Carlo Simulation Modeling of a Regional Stroke Team’s Use of Telemedicine Elham Torabi, MS, Craig M. Froehle, PhD, Christopher J. Lindsell, PhD, Charles J. Moomaw, PhD, Daniel Kanter, MD, Dawn Kleindorfer, MD, and Opeolu Adeoye, MD, MS Abstract Objectives: The objective of this study was to evaluate operational policies that may improve the proportion of eligible stroke patients within a population who would receive intravenous recombinant tissue plasminogen activator (rt-PA) and minimize time to treatment in eligible patients. Methods: In the context of a regional stroke team, the authors examined the effects of staff location and telemedicine deployment policies on the timeliness of thrombolytic treatment, and estimated the efficacy and cost-effectiveness of six different policies. A process map comprising the steps from recognition of stroke symptoms to intravenous administration of rt-PA was constructed using data from published literature combined with expert opinion.
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Six scenarios were investigated: telemedicine deployment (none, all, or outer-ring hospitals only) and staff location (center of region or anywhere in region). Physician locations were randomly generated based on their zip codes of residence and work. The outcomes of interest were onset-to-treatment (OTT) time, door-to-needle (DTN) time, and the proportion of patients treated within 3 hours. A Monte Carlo simulation of the stroke team care-delivery system was constructed based on a primary data set of 121 ischemic stroke patients who were potentially eligible for treatment with rt-PA. Results: With the physician located randomly in the region, deploying telemedicine at all hospitals in the region (compared with partial or no telemedicine) would result in the highest rates of treatment within 3 hours (80% vs. 75% vs. 70%) and the shortest OTT (148 vs. 164 vs. 176 minutes) and DTN (45 vs. 61 vs. 73 minutes) times. However, locating the on-call physician centrally coupled with partial telemedicine deployment (five of the 17 hospitals) would be most cost-effective with comparable eligibility and treatment times. Conclusions: Given the potential societal benefits, continued efforts to deploy telemedicine appear warranted. Aligning the incentives between those who would have to fund the up-front technology investments and those who will benefit over time from reduced ongoing health care expenses will be necessary to fully realize the benefits of telemedicine for stroke care. Walden University DDHA 8800 Simulation of Telemedicine Technologies. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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ACADEMIC EMERGENCY MEDICINE
2016;23:55–62 © 2015 by the Society for Academic Emergency Medicine A ppropriate treatment of ischemic stroke requires temporal urgency. Every 15-minute reduction in delay to treatment with recombinant tissue plasminogen activator (rt-PA) results in increased odds (odds ratio [OR] = 1.04; 95% confidence interval [CI] = 1.03 to 1.05; p < 0.001) of the patient being independent at hospital discharge.1,2 Despite this urgency, many patients do not get proper stroke care in a timely manner. At presumably highly motivated centers that participate in the American Stroke Association (ASA)’s “Get with the Guidelines” quality initiative, only half of all rt-PA–treated patients received treatment From the Lindner College of Business (ET, CMF), the Department of Emergency Medicine (ET, CMF, CJL, OA), the Department of Neurology and Rehabilitation Medicine (CJM, DKa, DKl), and the Department of Neurosurgery (OA), University of Cincinnati, Cincinnati, OH; Cincinnati Children’s Hospital Medical Center (CMF), Cincinnati, OH; and the University of Cincinnati Neuroscience Institute (DKa, DKl, OA), Cincinnati, OH. Received April 20, 2015; revision received July 27, 2015; accepted August 5, 2015. Dr. Kliendorfer was supported in part by an NIH-R01 grant. NINDS R01NS30678 was the source of primary data set that constituted the basis of the Monte Carlo simulation.
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NINDS had no control over the content or publication of this work. The authors have no potential conflicts to disclose. Supervising Editor: Peter Panagos, MD. Address for correspondence and reprints: Opeolu Adeoye, MD, MS; e-mail: Opeolu.Adeoye@uc.edu. © 2015 by the Society for Academic Emergency Medicine doi: 10.1111/acem.12839 ISSN 1069-6563 PII ISSN 1069-6563583 55 55 56 within the recommended 60 minutes from hospital arrival after a quality improvement intervention; just 26.5% achieved this goal preintervention.3 One approach to increasing the responsiveness of medical centers to stroke patients is to organize regional stroke teams offering clinical and technical support. In the Greater Cincinnati area, the stroke team has a stroke physician on call 24/7. Once notified of a potential candidate for treatment, the on-call physician typically travels to the hospital where the patient is located in order to provide care, while other clinical and diagnostic work-up proceeds. Although travel time from the stroke physician’s location to the patient’s bedside occurs in parallel with diagnostic and imaging work, long travel times have the potential to delay care. To provide treatment more rapidly, health care providers are turning to advanced telemedicine technologies. Telestroke provides stroke team physicians with enhanced communication with remote patients by providing a two-way, audio-visual connection with integrated electronic medical information, scans, and tests results, as well as clinical assessment tools. Telestroke can facilitate timely rt-PA treatment without lowering the quality of care.4
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However, the technology can be expensive, and deploying it at all care sites may not be financially viable, despite evidence that telestroke can be cost-effective in the long term.6 It seems likely that, under a constrained budget, equipping all hospitals in a region with telestroke units may be cost-prohibitive. Therefore, perhaps the farthest hospitals in a region, with the longest stroke physician travel times, should be the first locations to receive telestroke units. The travel distance to and, hence, time to treatment for patients at, sites without telemedicine will be affected by where the stroke physician is located when the call is received. It might be assumed that, if the stroke physician is located centrally, travel time is reduced across sites. Whether this holds true given the distribution of where stroke patients are treated is unknown. Torabi et al. • STROKE TEAM USE OF TELEMEDICINE We hypothesized that deploying telemedicine at a subset of five outlying hospitals in our region could be more cost-effective than deploying telemedicine at all hospitals in the region. We also hypothesized that the proportion of patients who could receive treatment within 3 hours would be increased. Finally, we expected that onset-to-treatment (OTT) time would be reduced when the stroke physician was centrally located compared to when the stroke physician was not centrally located. Walden University DDHA 8800 Simulation of Telemedicine Technologies
METHODS
Study Design This was a computer simulation study using Monte Carlo methodology. The study was funded in part by an unrestricted investigator initiated grant from Genentech, Inc. Genentech played no role in design, data acquisition, simulations, or drafting/revision of the manuscript. Since only previously deidentified data and simulation techniques were used, the study was deemed non–human subjects research by the University of Cincinnati Institutional Review Board. Study Protocol A high-level process map of the stroke care process from stroke onset to rt-PA treatment was first developed (Figure 1). A Monte Carlo simulation of the stroke-team care-delivery system was then constructed based on a primary data set of 121 ischemic stroke patients who were residents of the Greater Cincinnati/ Northern Kentucky Region during 2005, had a confirmed symptom onset time, presented within 4.5 hours of onset to a local study ED, and had no contraindications to receiving rt-PA. This region, which is representative of the United States in terms of age distribution, racial composition, level of education, and median household income, includes 17 acute care hospitals, all served by a single, highly experienced stroke team that Figure 1. High-level process map from stroke onset to rt-PA administration. Source: Authors’ depiction of normative ischemic stroke care process. Notes: Sizes of activity blocks are not scaled to represent time durations. rt-PA = recombinant tissue plasminogen activator.
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ACADEMIC EMERGENCY MEDICINE • January 2016, Vol. 23, No. 1 • www.aemj.org has offered acute stroke treatment and management for over 20 years. The primary data set was obtained from a population-based, epidemiology study of stroke, the Greater Cincinnati/Northern Kentucky Stroke Study (GCNKSS), which is described in detail previously.6 In brief, study nurses and physicians use comprehensive medical record review methodology to collect detailed clinical information for every hospitalized stroke for all residents of the region. We used these data to construct a model to estimate the effects of different operational policies on time-to-treatment within the population. Specifically, we modeled OTT time, door-to-needle (DTN) time, and the proportion of eligible patients receiving rt-PA within 3 hours of stroke onset. Process Map. The normative process modeled here starts from the time the patient recognizes the stroke has occurred (the recognition time). The patient then either takes a personal vehicle or calls an ambulance to obtain care. If the latter, a dispatch notice is then sent. An emergency medical services (EMS) team travels to the patient location, prepares the patient for transfer, and transports the patient to a nearby hospital. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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The EMS team may or may not prenotify the receiving hospital prior to arrival.5 After the patient arrives at the hospital, whether by ambulance or by personal vehicle, the ED staff perform an initial work-up. If the patient is recognized as a possible stroke patient, ED staff notify the stroke team. In cases where EMS preemptively notifies the hospital, ED staff may notify the stroke team and may facilitate an immediate computed tomography (CT) scan. While the patient may have blood work and a CT scan done, the determination of eligibility and administration of rt-PA begins only when the stroke team physician evaluates the patient. Once the stroke team physician is notified, he or she can travel to the hospital or set up a telemedicine consultation. For rtPA–eligible patients, the medicine is prepared either at bedside or through the pharmacy (depending on hospital policy); once it is prepared, treatment may commence. Sampled and Simulated Patient Populations. Table 1 summarizes the variables that were extracted from the GCNKSS data set and the statistical expressions that best describe the data. Arena Input Analyzer (Rockwell Automation, Inc., Milwaukee, WI) was employed to find the probability density function that fits the empirical data best for each variable. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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In cases where we did not have data, we relied on expert opinion to estimate minima, maxima, and modes of the variables of interest and then built triangular distributions for those variables (identified by asterisks in Table 1). An essential element of this analysis is identifying the location and travel times for both patients and stroke team physicians. For travel-time calculations, ArcGIS (ESRI, Redlands, CA) was used to randomly generate hypothetical patient locations throughout the five counties of Hamilton and Clermont in southwest Ohio and Kenton, Boone, and Campbell in northern Kentucky. One-hundred random locations within each of the 92 standard zip codes were generated and identified by latitude and longitude and the nearest street address. 57 Table 1 Process Variables and Their Best-fit Probability Density Functions Variable Probability Distribution Recognition time duration % Calling an ambulance Call ambulance duration EMS patient prep time duration Patient handover duration EMS prenotification rate5 Workup duration Duration from CT order to reading Bedside prep duration* Pharmacy prep duration* Additional tests and evaluations* Telemedicine set-up time duration* Delay in calling stroke team after patient arrival* Probability of stroke team at base hospital* ED triage time duration* Weibull (24.6, 0.479) 88% (deterministic) Gamma (1.41, 1.41)–0.5 Weibull (15, 2.09) Gamma (4.31, 1.45) 73% (deterministic) Weibull (18.4, 0.85) Gamma (10, 1.78) Triangular (2, 5, 8) Triangular (5, 10, 15) Triangular (2, 5, 8) Triangular (2, 5, 8) Uniform (5, 15) 0.42 (10 hours/day) Triangular (15, 30, 45) *Probability distributions per expert estimates (no * indicates distribution is based on 2005 GCNKSS data6).
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CT = computed tomography; EMS = emergency medical services; GCNKSS = Greater Cincinnati/Northern Kentucky Stroke Study. We note that 12 of the Ohio zip codes and five of the Kentucky zip codes cross the boundaries of our fivecounty region into adjacent counties. The goal of the GCNKSS is to determine population-based incidence of stroke; therefore, it does not include cases from the adjacent counties. Its stroke team, however, is consulted for all potential cases of stroke that present to the region’s hospitals, irrespective of a patient’s residence. Therefore, we included the entire areas of these zip codes for the simulation. Three additional Ohio zip codes that are primarily associated with adjacent counties and have less than 2% of their populations in Hamilton or Clermont counties were not included in the simulation. Figure 2 shows the map of the geographic sampling frame and the 9,200 simulated patient locations. This pool of 9,200 locations was then used as a sampling frame for both patients and physician locations, with physicians’ locations limited to those zip codes in which they live and work. The Google Maps application program interface was used to generate estimated travel durations for each of the patient and stroke physicians going to each hospital; code was written using Visual Basic for Applications to generate batch routing within Microsoft Excel.
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Travel time estimates were based on early afternoon weekday traffic densities, representative of “typical” travel times. This decreases the potential for extreme outliers due to rush hour. Monte Carlo Simulation. We examined two factors of interest. The first factor was telemedicine availability at various hospitals. Three different deployment policies were compared: 1) no telemedicine in the region; 2) telemedicine in all hospitals throughout the region; 3) telemedicine only in outer-ring hospitals. The second factor was the location of stroke team physicians while on call. We considered two policies: 58 Torabi et al. • STROKE TEAM USE OF TELEMEDICINE Figure 2. Seventeen hospital locations and 9,200 randomly generated patient locations in the Greater Cincinnati/Northern Kentucky regional sampling frame. Source: Authors’ data, generated using ArcGIS software. Notes: Hospital locations and zip code boundaries accurate as of October 31, 2014. 1) stroke team physicians were based in their home zip code and 2) stroke team physicians were located within a 15-minute driving radius of the center of the region. Full-factorial combination of these policies resulted in six distinct scenarios. The performance of each scenario was estimated using a Monte Carlo computer simulation model. The desired margin of error for comparing sample proportions was 0.01, requiring 7,000 simulated observations per scenario. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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To ensure reliable analysis of results, variance reduction was used to decrease statistical “noise” (unexplained variance) in the output measure of performance. This noise reduction helps in better capturing the effect of the two operational factors (telemedicine policy and physician location policy). The Monte Carlo simulation of the care process was modeled in Microsoft Excel and the Common Random Generation method was used for variance reduction.7 Below is a brief overview of one replication of the simulation model; the process was repeated for each patient until the desired number of replications was achieved for each scenario. Note that all of these steps can be found in the process map shown in Figure 1. All time durations were generated using the expressions shown in Table 1. The model was verified and validated against the 2005 GCNKSS data6 and expert opinion. Stroke Onset (Time 0): 1. Generate a patient location. Generate a uniform random number and look up corresponding location in the sampling data set. 2. Generate recognition duration. This is the amount of time between onset and the patient recognizing he requires medical attention. 3. Determine destination hospital. This was randomly selected from the three closest hospitals, where the chance of being selected was weighted based on proximity to the patient’s recognition location. 4. Generate traveling mode: personal vehicle or ambulance. We assumed all patients traveled directly to a hospital and did not seek care elsewhere first. 5. Generate travel time from patient location to destination hospital. If patient travels by ambulance, follow steps 6–10: 6. Generate “call ambulance” time. 7. Indicate ambulance-to-patient travel time assuming ambulance originates from destination hospital. 8. Generate EMS on-scene time. 9. Generate EMS prenotification:8 Yes/No; if yes, initial work-up duration, door-to-imaging time, and timeto-stroke team notification were adjusted to reflect ACADEMIC EMERGENCY MEDICINE • January 2016, Vol. 23, No. 1 • www.aemj.org the reduced times resulting from the EMS prenotification.
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Identify patient-to-hospital travel time. ED Arrival/Triage: 10. If patient is arriving by ambulance, generate patient-handover duration. 11. If patient is arriving by personal vehicle, generate triage delay. 12. Generate initial work-up time. 13. Generate the time stamp for when ED staff at destination hospital calls stroke team. 14. Generate location of stroke team physician according to the scenario policy. 15. Generate mode by which stroke team provides care: telemedicine or traveling to patient’s bedside. If traveling, look up travel time from stroke team location to destination hospital. If using telemedicine, generate telemedicine set-up duration. 16. Generate CT duration: time from CT order to when preliminary results are available. Stroke Team Care Begins: 17. Generate duration for final tests and evaluation. 18. Generate rt-PA preparation time; either bedside preparation or pharmacy preparation, per each hospital’s policy. 19. Collect time of treatment for patient; calculate the OTT time and DTN time. 20. Repeat steps 1 to 19 for n patients, where n is the number of replications needed to achieve target margins of error on output measures of performance (in our case, 7,000 patients per scenario). Return-on-investment Analysis. The final component of our analysis was to roughly estimate the economic return, in terms of payback period, for a region should it decide to deploy telemedicine at some or all hospitals in the region. We considered two scenarios consistent with the prior model assumptions: 1) partial deployment, where only the outermost five hospitals receive telemedicine, and 2) full deployment, where all hospitals in the region receive telemedicine. Walden University DDHA 8800 Simulation of Telemedicine Technologies
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Recent cost-effectiveness studies estimate that treatment with rt-PA within 59 3 hours of stroke onset results in an average lifetime societal savings of $25,000 per patient.9 Combining that figure with our model’s output and a range of telemedicine costs from $1,000 per location to $50,000 per location yielded overall payback curves that indicate how long it is likely to take to recoup the cost of the telemedicine in terms of reductions in stroke-related morbidity and mortality. All technology costs were assumed to occur up front … Walden University DDHA 8800 Simulation of Telemedicine Technologies
